{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cf2ef7a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tushare as ts\n",
    "import re\n",
    "import os\n",
    "import time\n",
    "from sqlalchemy import text,create_engine\n",
    "from funcs import *\n",
    "from tqdm.notebook import tqdm\n",
    "from loguru import logger\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] \n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "token = '1501ffe708345cffa38d9bbc0bd371e93b4b7412e7a8e1f811d3c442'\n",
    "ts.set_token(token)\n",
    "pro = ts.pro_api(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f03dd22",
   "metadata": {},
   "outputs": [],
   "source": [
    "期货前缀ss = os.listdir('D:/tushare_database/dailydata')\n",
    "期货前缀ss = [x.split('.')[0] for x in 期货前缀ss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93b4b307",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d73d6b62831a4db9b65bfe9fb57bbf84",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/86 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "期货前缀ss2 = []\n",
    "for 期货前缀 in tqdm(期货前缀ss):\n",
    "    df = get_df(期货前缀)\n",
    "    df_close = df.pivot(index='t', columns='code', values='close')\n",
    "#     if (df_close.notnull().sum(1) != 0).sum() > 250 and df_close.columns[-1] > '2301':\n",
    "    if (df_close.notnull().sum(1) != 0).sum() > 250:\n",
    "        期货前缀ss2.append(期货前缀)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "76a86c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(期货前缀ss2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fd2efcb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46ed1a3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c967884d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c1bf9050cf44f229c527ec7699a9139",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/77 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddds = []\n",
    "for 期货前缀 in tqdm(期货前缀ss2):\n",
    "    df = get_df(期货前缀)\n",
    "    df_close = df.pivot(index='t', columns='code', values='close')\n",
    "    df_money = df.pivot(index='t', columns='code', values='amount')\n",
    "    当前主力代码 = pd.Series(df_money.columns[np.argsort(-df_money.values,axis=1)[:,0]],\n",
    "                       index=df_money.index).shift(1)\n",
    "    \n",
    "    ddd = pd.concat([\n",
    "        当前主力代码,\n",
    "        df_close.apply(lambda x: x.get(当前主力代码.get(x.name)), axis=1),\n",
    "        df_close.pct_change().apply(lambda x: x.get(当前主力代码.get(x.name)), axis=1),\n",
    "        df_money.sum(1)\n",
    "    ], axis=1)\n",
    "    ddd.columns = [\n",
    "        '主力代码','主力close','主力代码收益率','交易额'\n",
    "    ]\n",
    "    ddds.append(ddd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "340d5a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff_ret = pd.concat([x['主力代码收益率'] for x in ddds],axis=1)\n",
    "dff_ret.columns = 期货前缀ss2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4a38bfbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff_money = pd.concat([x['交易额'] for x in ddds],axis=1)\n",
    "dff_money.columns = 期货前缀ss2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d5c769d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC    3.250184e+08\n",
       "AG    2.135973e+08\n",
       "AU    2.102319e+08\n",
       "TS    2.087541e+08\n",
       "IF    1.922659e+08\n",
       "T     1.919787e+08\n",
       "IM    1.812329e+08\n",
       "SA    1.796375e+08\n",
       "RB    1.677349e+08\n",
       "IC    1.564196e+08\n",
       "Y     1.424837e+08\n",
       "TF    1.413075e+08\n",
       "OI    1.355322e+08\n",
       "P     1.282636e+08\n",
       "I     1.186099e+08\n",
       "TA    1.160025e+08\n",
       "M     1.137756e+08\n",
       "RU    1.106648e+08\n",
       "NI    1.089231e+08\n",
       "CF    9.745877e+07\n",
       "CU    8.791951e+07\n",
       "FG    8.513529e+07\n",
       "SR    8.156668e+07\n",
       "SP    7.891659e+07\n",
       "FU    7.444718e+07\n",
       "IH    7.354957e+07\n",
       "MA    6.912172e+07\n",
       "V     6.350491e+07\n",
       "RM    6.118919e+07\n",
       "PG    6.007080e+07\n",
       "EB    5.835450e+07\n",
       "HC    5.407902e+07\n",
       "SN    5.266749e+07\n",
       "JM    4.944160e+07\n",
       "AL    4.919191e+07\n",
       "UR    4.713949e+07\n",
       "ZN    4.465871e+07\n",
       "LH    3.980806e+07\n",
       "PP    3.628273e+07\n",
       "SS    3.550032e+07\n",
       "L     2.998160e+07\n",
       "C     2.740874e+07\n",
       "BU    2.709136e+07\n",
       "EG    2.692730e+07\n",
       "J     2.122258e+07\n",
       "LU    2.093449e+07\n",
       "AP    1.918222e+07\n",
       "PF    1.805746e+07\n",
       "NR    1.800529e+07\n",
       "SF    1.775669e+07\n",
       "Name: 2023-12-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff_money.rolling(20).sum().loc['2023-12-04'].sort_values(ascending=False)[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9c923108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2008-07-15             NaN\n",
       "2008-07-16             NaN\n",
       "2008-07-17             NaN\n",
       "2008-07-18             NaN\n",
       "2008-07-21             NaN\n",
       "                  ...     \n",
       "2023-11-28   -5.786660e-11\n",
       "2023-11-29   -5.786660e-11\n",
       "2023-11-30   -5.786660e-11\n",
       "2023-12-01   -5.786660e-11\n",
       "2023-12-04   -5.786660e-11\n",
       "Name: RI, Length: 3742, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff_money['RI'].rolling(2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "11a91fd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff_money['RI'][-21:].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "88019d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>AG</th>\n",
       "      <th>AL</th>\n",
       "      <th>AP</th>\n",
       "      <th>AU</th>\n",
       "      <th>B</th>\n",
       "      <th>BB</th>\n",
       "      <th>BC</th>\n",
       "      <th>BU</th>\n",
       "      <th>C</th>\n",
       "      <th>...</th>\n",
       "      <th>TS</th>\n",
       "      <th>UR</th>\n",
       "      <th>V</th>\n",
       "      <th>WH</th>\n",
       "      <th>WR</th>\n",
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       "      <th>WT</th>\n",
       "      <th>Y</th>\n",
       "      <th>ZC</th>\n",
       "      <th>ZN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-07-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2008-07-16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-18</th>\n",
       "      <td>9.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-21</th>\n",
       "      <td>61.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-28</th>\n",
       "      <td>539452.63</td>\n",
       "      <td>11648285.97</td>\n",
       "      <td>2334096.11</td>\n",
       "      <td>1127867.16</td>\n",
       "      <td>9135776.75</td>\n",
       "      <td>383445.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>512909.02</td>\n",
       "      <td>1111001.96</td>\n",
       "      <td>1466558.43</td>\n",
       "      <td>...</td>\n",
       "      <td>1.018455e+07</td>\n",
       "      <td>1553072.19</td>\n",
       "      <td>2277101.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>485.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5378423.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1998607.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-29</th>\n",
       "      <td>525559.44</td>\n",
       "      <td>14420673.00</td>\n",
       "      <td>3974428.63</td>\n",
       "      <td>993086.67</td>\n",
       "      <td>13629086.09</td>\n",
       "      <td>441890.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>524129.43</td>\n",
       "      <td>1617962.04</td>\n",
       "      <td>1062462.60</td>\n",
       "      <td>...</td>\n",
       "      <td>1.205444e+07</td>\n",
       "      <td>1676858.48</td>\n",
       "      <td>2324361.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>258.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5716007.41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2126566.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-30</th>\n",
       "      <td>703264.75</td>\n",
       "      <td>11520506.96</td>\n",
       "      <td>2387117.52</td>\n",
       "      <td>818366.66</td>\n",
       "      <td>9624254.47</td>\n",
       "      <td>463959.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>526458.25</td>\n",
       "      <td>1218904.86</td>\n",
       "      <td>945470.76</td>\n",
       "      <td>...</td>\n",
       "      <td>8.112669e+06</td>\n",
       "      <td>1312127.17</td>\n",
       "      <td>2878815.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>863.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4566298.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2408802.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-01</th>\n",
       "      <td>854333.00</td>\n",
       "      <td>8528151.21</td>\n",
       "      <td>1859177.56</td>\n",
       "      <td>636981.37</td>\n",
       "      <td>9173108.27</td>\n",
       "      <td>567953.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>533160.69</td>\n",
       "      <td>1472135.69</td>\n",
       "      <td>1224084.24</td>\n",
       "      <td>...</td>\n",
       "      <td>6.628805e+06</td>\n",
       "      <td>2393624.47</td>\n",
       "      <td>4417712.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>327.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6450175.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1623715.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-04</th>\n",
       "      <td>662255.66</td>\n",
       "      <td>12362558.84</td>\n",
       "      <td>2091122.72</td>\n",
       "      <td>781971.78</td>\n",
       "      <td>15690061.98</td>\n",
       "      <td>494344.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>612560.98</td>\n",
       "      <td>1226648.85</td>\n",
       "      <td>1337052.35</td>\n",
       "      <td>...</td>\n",
       "      <td>5.133470e+06</td>\n",
       "      <td>1418834.90</td>\n",
       "      <td>3274849.51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2056.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5258757.53</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1501144.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3742 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    A           AG          AL          AP           AU  \\\n",
       "t                                                                         \n",
       "2008-07-15        NaN          NaN         NaN         NaN          NaN   \n",
       "2008-07-16        NaN          NaN         NaN         NaN          NaN   \n",
       "2008-07-17        NaN          NaN         NaN         NaN          NaN   \n",
       "2008-07-18       9.14          NaN         NaN         NaN          NaN   \n",
       "2008-07-21      61.98          NaN         NaN         NaN          NaN   \n",
       "...               ...          ...         ...         ...          ...   \n",
       "2023-11-28  539452.63  11648285.97  2334096.11  1127867.16   9135776.75   \n",
       "2023-11-29  525559.44  14420673.00  3974428.63   993086.67  13629086.09   \n",
       "2023-11-30  703264.75  11520506.96  2387117.52   818366.66   9624254.47   \n",
       "2023-12-01  854333.00   8528151.21  1859177.56   636981.37   9173108.27   \n",
       "2023-12-04  662255.66  12362558.84  2091122.72   781971.78  15690061.98   \n",
       "\n",
       "                    B   BB         BC          BU           C  ...  \\\n",
       "t                                                              ...   \n",
       "2008-07-15        NaN  NaN        NaN         NaN         NaN  ...   \n",
       "2008-07-16        NaN  NaN        NaN         NaN         NaN  ...   \n",
       "2008-07-17        NaN  NaN        NaN         NaN         NaN  ...   \n",
       "2008-07-18        NaN  NaN        NaN         NaN         NaN  ...   \n",
       "2008-07-21        NaN  NaN        NaN         NaN         NaN  ...   \n",
       "...               ...  ...        ...         ...         ...  ...   \n",
       "2023-11-28  383445.46  0.0  512909.02  1111001.96  1466558.43  ...   \n",
       "2023-11-29  441890.85  0.0  524129.43  1617962.04  1062462.60  ...   \n",
       "2023-11-30  463959.86  0.0  526458.25  1218904.86   945470.76  ...   \n",
       "2023-12-01  567953.89  0.0  533160.69  1472135.69  1224084.24  ...   \n",
       "2023-12-04  494344.14  0.0  612560.98  1226648.85  1337052.35  ...   \n",
       "\n",
       "                      TS          UR           V   WH       WR  WS  WT  \\\n",
       "t                                                                        \n",
       "2008-07-15           NaN         NaN         NaN  NaN      NaN NaN NaN   \n",
       "2008-07-16           NaN         NaN         NaN  NaN      NaN NaN NaN   \n",
       "2008-07-17           NaN         NaN         NaN  NaN      NaN NaN NaN   \n",
       "2008-07-18           NaN         NaN         NaN  NaN      NaN NaN NaN   \n",
       "2008-07-21           NaN         NaN         NaN  NaN      NaN NaN NaN   \n",
       "...                  ...         ...         ...  ...      ...  ..  ..   \n",
       "2023-11-28  1.018455e+07  1553072.19  2277101.07  0.0   485.39 NaN NaN   \n",
       "2023-11-29  1.205444e+07  1676858.48  2324361.75  0.0   258.25 NaN NaN   \n",
       "2023-11-30  8.112669e+06  1312127.17  2878815.16  0.0   863.37 NaN NaN   \n",
       "2023-12-01  6.628805e+06  2393624.47  4417712.86  0.0   327.02 NaN NaN   \n",
       "2023-12-04  5.133470e+06  1418834.90  3274849.51  0.0  2056.91 NaN NaN   \n",
       "\n",
       "                     Y   ZC          ZN  \n",
       "t                                        \n",
       "2008-07-15         NaN  NaN         NaN  \n",
       "2008-07-16         NaN  NaN         NaN  \n",
       "2008-07-17         NaN  NaN         NaN  \n",
       "2008-07-18         NaN  NaN         NaN  \n",
       "2008-07-21         NaN  NaN         NaN  \n",
       "...                ...  ...         ...  \n",
       "2023-11-28  5378423.73  0.0  1998607.26  \n",
       "2023-11-29  5716007.41  0.0  2126566.86  \n",
       "2023-11-30  4566298.24  0.0  2408802.74  \n",
       "2023-12-01  6450175.23  0.0  1623715.59  \n",
       "2023-12-04  5258757.53  0.0  1501144.20  \n",
       "\n",
       "[3742 rows x 77 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff_money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03ad111d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74760241",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cff17220",
   "metadata": {},
   "outputs": [],
   "source": [
    "code = 'USDCNH.FXCM'\n",
    "dd_fx = pro.fx_daily(ts_code=code)\n",
    "dd_fx.index = pd.to_datetime(dd_fx['trade_date'])\n",
    "dd_fx['open'] = (dd_fx['ask_open'] + dd_fx['bid_open'])/2\n",
    "dd_fx['high'] = (dd_fx['ask_high'] + dd_fx['bid_high'])/2\n",
    "dd_fx['low'] = (dd_fx['ask_low'] + dd_fx['bid_low'])/2\n",
    "dd_fx['close'] = (dd_fx['ask_close'] + dd_fx['bid_close'])/2\n",
    "dd_fx = dd_fx[['open','high','low','close','tick_qty']]\n",
    "dd_fx = dd_fx.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c5700773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c206ef370>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dd_fx['close'].pct_change().cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6e7bd599",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c21839940>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dff_ret.mean(1).cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d8dc1f06",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c21d9fa60>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "金融期货代码 = ['IF','IH','IC','IM','TS','TF','T']\n",
    "dff_ret.drop(金融期货代码,axis=1).mean(1).cumsum().plot()\n",
    "dd_fx['close'].pct_change().cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "64e9ba90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.203313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>-0.203313</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0     close\n",
       "0      1.000000 -0.203313\n",
       "close -0.203313  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['RS', 'A', 'ZC', 'ER', 'ME', 'SS', 'RU', 'CY', 'V', 'BU', 'CU', 'BC', 'P', 'JM', 'FG', 'AU', 'MA', 'EG', 'UR', 'SM', 'PK', 'Y', 'SC', 'TC', 'SP', 'RM', 'RR', 'JR', 'FB', 'I', 'M', 'BB', 'CJ', 'SR', 'LH', 'RI', 'C', 'SN', 'IM', 'AG', 'LR', 'NI', 'NR', 'RO', 'PG', 'CS', 'OI', 'CF', 'WS', 'J', 'RB', 'AL', 'WH', 'SA', 'SF', 'B', 'PM', 'FU', 'ZN', 'LU', 'JD', 'WT', 'TA', 'PF', 'PB', 'AP', 'L', 'WR', 'HC', 'PP', 'EB']\n",
    "the_date = '2012-01-01'\n",
    "xx1 = dff_ret.drop(金融期货代码,axis=1).mean(1)[the_date:]\n",
    "xx1 = dff_ret.mean(1)[the_date:]\n",
    "xx1 = dff_ret[cols].mean(1)[the_date:]\n",
    "xx2 = dd_fx['close'].pct_change()[the_date:]\n",
    "\n",
    "pd.concat([xx1,xx2],axis=1)['2014-01-01':].fillna(0).resample('M').sum().corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb152995",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RS</th>\n",
       "      <th>A</th>\n",
       "      <th>ZC</th>\n",
       "      <th>ER</th>\n",
       "      <th>ME</th>\n",
       "      <th>SS</th>\n",
       "      <th>RU</th>\n",
       "      <th>CY</th>\n",
       "      <th>V</th>\n",
       "      <th>BU</th>\n",
       "      <th>...</th>\n",
       "      <th>WT</th>\n",
       "      <th>TA</th>\n",
       "      <th>PF</th>\n",
       "      <th>PB</th>\n",
       "      <th>AP</th>\n",
       "      <th>L</th>\n",
       "      <th>WR</th>\n",
       "      <th>HC</th>\n",
       "      <th>PP</th>\n",
       "      <th>EB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-07-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.039606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-28</th>\n",
       "      <td>0.006153</td>\n",
       "      <td>-0.001782</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011355</td>\n",
       "      <td>0.001076</td>\n",
       "      <td>0.011133</td>\n",
       "      <td>-0.005260</td>\n",
       "      <td>0.006700</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007400</td>\n",
       "      <td>-0.005001</td>\n",
       "      <td>-0.000619</td>\n",
       "      <td>0.017706</td>\n",
       "      <td>-0.003674</td>\n",
       "      <td>0.005814</td>\n",
       "      <td>-0.010358</td>\n",
       "      <td>-0.004162</td>\n",
       "      <td>-0.023549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-29</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010866</td>\n",
       "      <td>-0.023297</td>\n",
       "      <td>0.027404</td>\n",
       "      <td>-0.001706</td>\n",
       "      <td>0.020799</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.004970</td>\n",
       "      <td>-0.003072</td>\n",
       "      <td>-0.007127</td>\n",
       "      <td>-0.002626</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>0.000963</td>\n",
       "      <td>-0.001744</td>\n",
       "      <td>-0.003370</td>\n",
       "      <td>0.000249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005332</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.008957</td>\n",
       "      <td>-0.000367</td>\n",
       "      <td>0.001429</td>\n",
       "      <td>0.006494</td>\n",
       "      <td>0.002445</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026044</td>\n",
       "      <td>0.014846</td>\n",
       "      <td>-0.013109</td>\n",
       "      <td>0.003291</td>\n",
       "      <td>0.012581</td>\n",
       "      <td>-0.008181</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.011227</td>\n",
       "      <td>0.013299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.009761</td>\n",
       "      <td>-0.016520</td>\n",
       "      <td>-0.013793</td>\n",
       "      <td>-0.008149</td>\n",
       "      <td>-0.005420</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.015647</td>\n",
       "      <td>-0.011593</td>\n",
       "      <td>-0.001581</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>-0.004518</td>\n",
       "      <td>0.008006</td>\n",
       "      <td>0.004492</td>\n",
       "      <td>-0.004548</td>\n",
       "      <td>-0.024408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-04</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.007805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.006572</td>\n",
       "      <td>-0.007092</td>\n",
       "      <td>-0.006993</td>\n",
       "      <td>-0.021054</td>\n",
       "      <td>-0.016349</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.003179</td>\n",
       "      <td>-0.002234</td>\n",
       "      <td>-0.008869</td>\n",
       "      <td>-0.010713</td>\n",
       "      <td>-0.009581</td>\n",
       "      <td>0.032010</td>\n",
       "      <td>-0.012884</td>\n",
       "      <td>-0.011019</td>\n",
       "      <td>-0.014207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3742 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  RS         A  ZC  ER  ME        SS        RU        CY  \\\n",
       "t                                                                          \n",
       "2008-07-15       NaN       NaN NaN NaN NaN       NaN       NaN       NaN   \n",
       "2008-07-16       NaN       NaN NaN NaN NaN       NaN       NaN       NaN   \n",
       "2008-07-17       NaN       NaN NaN NaN NaN       NaN       NaN       NaN   \n",
       "2008-07-18       NaN       NaN NaN NaN NaN       NaN       NaN       NaN   \n",
       "2008-07-21       NaN -0.039606 NaN NaN NaN       NaN       NaN       NaN   \n",
       "...              ...       ...  ..  ..  ..       ...       ...       ...   \n",
       "2023-11-28  0.006153 -0.001782 NaN NaN NaN  0.011355  0.001076  0.011133   \n",
       "2023-11-29  0.000000  0.004363 NaN NaN NaN  0.010866 -0.023297  0.027404   \n",
       "2023-11-30       NaN -0.005332 NaN NaN NaN -0.008957 -0.000367  0.001429   \n",
       "2023-12-01       NaN -0.007941 NaN NaN NaN -0.009761 -0.016520 -0.013793   \n",
       "2023-12-04  0.000000 -0.007805 NaN NaN NaN -0.006572 -0.007092 -0.006993   \n",
       "\n",
       "                   V        BU  ...  WT        TA        PF        PB  \\\n",
       "t                               ...                                     \n",
       "2008-07-15       NaN       NaN  ... NaN       NaN       NaN       NaN   \n",
       "2008-07-16       NaN       NaN  ... NaN       NaN       NaN       NaN   \n",
       "2008-07-17       NaN       NaN  ... NaN       NaN       NaN       NaN   \n",
       "2008-07-18       NaN       NaN  ... NaN       NaN       NaN       NaN   \n",
       "2008-07-21       NaN       NaN  ... NaN       NaN       NaN       NaN   \n",
       "...              ...       ...  ...  ..       ...       ...       ...   \n",
       "2023-11-28 -0.005260  0.006700  ... NaN -0.007400 -0.005001 -0.000619   \n",
       "2023-11-29 -0.001706  0.020799  ... NaN -0.004970 -0.003072 -0.007127   \n",
       "2023-11-30  0.006494  0.002445  ... NaN  0.026044  0.014846 -0.013109   \n",
       "2023-12-01 -0.008149 -0.005420  ... NaN -0.015647 -0.011593 -0.001581   \n",
       "2023-12-04 -0.021054 -0.016349  ... NaN -0.003179 -0.002234 -0.008869   \n",
       "\n",
       "                  AP         L        WR        HC        PP        EB  \n",
       "t                                                                       \n",
       "2008-07-15       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2008-07-16       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2008-07-17       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2008-07-18       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2008-07-21       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "...              ...       ...       ...       ...       ...       ...  \n",
       "2023-11-28  0.017706 -0.003674  0.005814 -0.010358 -0.004162 -0.023549  \n",
       "2023-11-29 -0.002626  0.000636  0.000963 -0.001744 -0.003370  0.000249  \n",
       "2023-11-30  0.003291  0.012581 -0.008181  0.000250  0.011227  0.013299  \n",
       "2023-12-01  0.000328 -0.004518  0.008006  0.004492 -0.004548 -0.024408  \n",
       "2023-12-04 -0.010713 -0.009581  0.032010 -0.012884 -0.011019 -0.014207  \n",
       "\n",
       "[3742 rows x 71 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff_ret[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "047444c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x138b0d2bbe0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dff_ret[cols][the_date:].mean(1).cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "70deacc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.090000000000001"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2.7-0.4+0.83+1.55+2.41"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6c58796c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.196125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>0.196125</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          close     close\n",
       "close  1.000000  0.196125\n",
       "close  0.196125  1.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([xx2.resample('M').sum(),xx2.resample('M').sum().shift(1)],axis=1).corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "88afd9a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.206675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.206675</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1\n",
       "0  1.000000  0.206675\n",
       "1  0.206675  1.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([xx1.resample('M').sum(),xx1.resample('M').sum().shift(1)],axis=1).corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1739996e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2012-01-04   -0.001374\n",
       "2012-01-05    0.007496\n",
       "2012-01-06   -0.009537\n",
       "2012-01-09    0.001596\n",
       "2012-01-10    0.012796\n",
       "                ...   \n",
       "2023-11-28   -0.000673\n",
       "2023-11-29   -0.011449\n",
       "2023-11-30   -0.007973\n",
       "2023-12-01   -0.007960\n",
       "2023-12-04    0.007365\n",
       "Length: 2896, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff_ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3f5d701",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b616dbd3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CY      -0.315770\n",
       "AP      -0.311534\n",
       "SM      -0.273668\n",
       "SC      -0.268448\n",
       "B       -0.266453\n",
       "           ...   \n",
       "TL      -0.051206\n",
       "TC      -0.034828\n",
       "AO      -0.022602\n",
       "LC       0.002390\n",
       "close    1.000000\n",
       "Name: close, Length: 78, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([dd_fx['close'].pct_change(),dff_ret],axis=1)['2014-01-01':].resample('M').sum().\\\n",
    "corr().loc['close'].sort_values().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b0fcf12",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8a9ab094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x138acc76880>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dff_ret['CU'].cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7be0336",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd5a692d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cab10f5d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6dd99ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51114b9c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "eaef233c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>tick_qty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-02-18</th>\n",
       "      <td>6.301000</td>\n",
       "      <td>6.301000</td>\n",
       "      <td>6.301000</td>\n",
       "      <td>6.301000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-20</th>\n",
       "      <td>6.301000</td>\n",
       "      <td>6.301000</td>\n",
       "      <td>6.287450</td>\n",
       "      <td>6.294350</td>\n",
       "      <td>6605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-21</th>\n",
       "      <td>6.294350</td>\n",
       "      <td>6.302500</td>\n",
       "      <td>6.292400</td>\n",
       "      <td>6.298850</td>\n",
       "      <td>11092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-22</th>\n",
       "      <td>6.298850</td>\n",
       "      <td>6.302750</td>\n",
       "      <td>6.294750</td>\n",
       "      <td>6.298450</td>\n",
       "      <td>9891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-23</th>\n",
       "      <td>6.298450</td>\n",
       "      <td>6.302100</td>\n",
       "      <td>6.295550</td>\n",
       "      <td>6.298150</td>\n",
       "      <td>9202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-29</th>\n",
       "      <td>7.143130</td>\n",
       "      <td>7.157330</td>\n",
       "      <td>7.122810</td>\n",
       "      <td>7.144600</td>\n",
       "      <td>522710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-30</th>\n",
       "      <td>7.144600</td>\n",
       "      <td>7.155850</td>\n",
       "      <td>7.122835</td>\n",
       "      <td>7.124045</td>\n",
       "      <td>301089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-02</th>\n",
       "      <td>7.124045</td>\n",
       "      <td>7.128255</td>\n",
       "      <td>7.123725</td>\n",
       "      <td>7.127710</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-03</th>\n",
       "      <td>7.127710</td>\n",
       "      <td>7.155180</td>\n",
       "      <td>7.123725</td>\n",
       "      <td>7.149600</td>\n",
       "      <td>483522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-04</th>\n",
       "      <td>7.149600</td>\n",
       "      <td>7.153915</td>\n",
       "      <td>7.144655</td>\n",
       "      <td>7.150610</td>\n",
       "      <td>157174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3559 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                open      high       low     close  tick_qty\n",
       "trade_date                                                  \n",
       "2012-02-18  6.301000  6.301000  6.301000  6.301000         0\n",
       "2012-02-20  6.301000  6.301000  6.287450  6.294350      6605\n",
       "2012-02-21  6.294350  6.302500  6.292400  6.298850     11092\n",
       "2012-02-22  6.298850  6.302750  6.294750  6.298450      9891\n",
       "2012-02-23  6.298450  6.302100  6.295550  6.298150      9202\n",
       "...              ...       ...       ...       ...       ...\n",
       "2023-11-29  7.143130  7.157330  7.122810  7.144600    522710\n",
       "2023-11-30  7.144600  7.155850  7.122835  7.124045    301089\n",
       "2023-12-02  7.124045  7.128255  7.123725  7.127710       418\n",
       "2023-12-03  7.127710  7.155180  7.123725  7.149600    483522\n",
       "2023-12-04  7.149600  7.153915  7.144655  7.150610    157174\n",
       "\n",
       "[3559 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_fx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "175ab65e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ade2118",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ae6c8bcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14,6))\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.plot(xx1.resample('M').sum().cumsum())\n",
    "ax1.grid()\n",
    "ax2 = ax1.twinx() \n",
    "ax2.plot(xx2.resample('M').sum().cumsum(),'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "507c2fa0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
